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Transcript of 2-SiMDoM: A 2 Sieve Model for Detection of Mitosis in Multispectral Breast Cancer Imagery

2-SiMDoM: A 2 Sieve Model for Detection of Mitosis in Multispectral Breast Cancer ImageryArdhendu Tripathi*, Atin Mathur*, Mohit Daga*, Manohar Kuse^, Oscar C. Au^The LNM Institute of Information Technology, Jaipur, India*, Hong Kong University of Science and Technology, Hong Kong^17 September, 2013at20th IEEE International Conference on Image Processing, Melbourne, AustraliaRelevance of Mitotic Count in Breast Cancer Prognosis"All change is a miracle to contemplate, but it is a miracle which is taking place every instant" -Henry David ThoreauChallengesa) Mitosisb) Mitosisc) Non Mitosisd) Non MitosisDataset DescriptionMITOS Dataset (ICPR 2012 Mitosis Detection Dataset)48 slidesAll slides were H&E stained.Multispectral imagingImages were captured over 10 visible bands.For each band digitization was performed over 17 different focus planes resulting in stacks.Each focus plane was separated by 500 nm.System DesignPreprocessing and SegmentationFeature Extraction and Selection Handling Imbalanced Dataset2-Sieve ClassificationSchematic for Preprocessing and SegmentationEntropy based stack selectionImage entropy is defined as follows:The best quality stack was selected such that:The most informative stack for each band was used for all future computations.Cell SegmentationThe region based active contour model as proposed by Chan et. al. was employed.This technique was effective here due to the difference in the average pixel intensity levels inside and outside the cell. To obtain the seed points, the histogram equalized higher visible contrast band BB07 was used.

Feature ExtractionTraining was based on texture features5 GLCM features were computed in the wavelet domain (4 components - LL, LH, HL, HH).3 level decomposition chosen based on the minimum entropy algorithm.Total number of features = 5 X 4 X 3 X 10 bands = 600 9 additional GLEM features were extracted resulting in 90 features (9 X 10 bands).Optimal feature set selectionFeature set selection to get rid of statistically irrelevant set of features.Our aim - to find out the set of features which best discriminates the mitosis from non-mitosis.Supervised Dimensionality Selection based on Neighborhood Examination (SDSNE)Selects the set of features which minimizes the error rate i.e. the number of misclassified mitosis on the basis of the neighborhood majority rule in the feature space.690 initial features reduced to 43 Data ImbalanceImbalanced dataset causes inaccurate training of the classifier.Biased towards the majority class.Imbalanced dataset dealt by:-1) Oversampling of Mitotic instances2) Data CleaningOversampling of Mitotic instancesTo balance the dataset oversampling of the mitotic instances was done based upon:a) Perturbations - imageb) SMOTE (Synthetic Minority Oversampling Technique) - feature spaceData CleaningRemoval of class label noise and borderline examples. Instances participating in Tomek Links were eliminated.Tomek links - points that are each others closest neighbors but do not share the same label.The 2-Sieve ModelFirst Level SieveSVM with radial basis kernel.Trained on 33 HPF's and tested on 15 HPF's.Sensitivity = 85.29% and PPV = 59.58%.Second Level SieveTextural differences around a mitotic and a non-mitotic cell. a, b - mitosis and c, d - non-mitosis A set of textural features were extracted from a window of 100 X 100 around the bounding box of each segmented cell.Second Level Sieve contd.48 phase gradient and 48 gabor features extracted.3 bands used - Red, Green, Blue.For each of these 96 features, 4 statistical measures calculated:-a) meanb) skewnessc) kurtosisd) standard deviationTraining set - All Mitosis (after First Level Sieve) + Equal number of randomly selected non mitotic instances from the original dataset.Second Level Sieve Contd.An ensemble of Random Projections and SVM (linear kernel) with a majority rule was used for final mitosis prediction.Final Sensitivity = 82.35% Final PPV = 73.04%Experimental ResultsICPR 2012 Mitosis Detection Contest ParticipantsSDSNE (Supervised Dimensionality Selection based on Neighborhood Examination)Quantitative EvaluationQualitative EvaluationSegmentation and Data ImbalanceThe mean and the standard deviation of the distance between the detected mitosis and the ground truth centroids were found out to be 0.87 pixel and 0.45 pixel respectively.An experiment was carried out to test the confidence limit of our proposed scheme: Testing data: HPF's having less than or equal to 2 mitosisSensitivity = 81.13% and PPV = 74.97%1. Nottingham Grading Scheme:a) Mitotic countb) Nuclear Atypiac) Tubule formation2. Some studies suggest that mitosis count can be as predictive as the grading systemMitosis - Small objects with large variation in shapes.Similar looking lymphoid/inflammmatory and apoptic cells.High degree of dataset imbalance.IEEE Signal Processing SocietyThe LNM Institute of Information Technology, Jaipur, India.AcknowledgmentsTraining - 33 slidesTesting - 15 slidesSeeds for Active contourBB-07BB-06Histogram EqualizationGray Level ThresholdingSmoothing blobsEntropy based stack selectionBand SelectionActive contours without edges - Tony F. Chan and Luminita A. VeseFeature SetOptimal Feature SetMitosisNon- MitosisSMOTE + Tomek Links Removal